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ECCouncil
The EC‑Council Certified Big Data Consultant (BDC) exam validates an associate‑level professional’s ability to design, implement, and secure big data architectures using fundamentals, NoSQL, cloud services, and processing pipelines.
90
Minutes
50
Questions
70/100
Passing Score
$250
Exam Cost
Who Should Take This
It is intended for data engineers, solution architects, or IT consultants who have at least two years of experience working with data platforms and want to deepen their expertise in big data strategy, security, and cloud integration. These professionals seek formal recognition to advise clients on scalable, compliant data solutions.
What's Covered
1
Big Data Fundamentals
2
Data Architecture
3
Data Processing
4
NoSQL Databases
5
Cloud Big Data
6
Data Analytics
7
Big Data Security
8
Data Governance
9
ML Integration
10
Consulting and Strategy
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
Big Data Fundamentals
2 topics
Big data concepts
- Identify big data characteristics including volume velocity variety veracity and value and their implications for data management and analysis.
- Apply big data concepts to classify data types storage requirements and processing approaches for organizational data challenges.
- Analyze big data use cases to evaluate technology suitability infrastructure requirements and expected business value.
Ecosystem overview
- Describe big data technology ecosystem including Hadoop Spark Kafka NoSQL databases and cloud analytics services and their roles.
- Apply technology selection criteria to determine appropriate big data tools based on data type volume and processing requirements.
- Analyze technology ecosystem trade-offs to evaluate performance scalability cost and operational complexity for implementations.
2
Data Architecture
2 topics
Distributed storage
- Describe distributed storage systems including HDFS object storage data lakes and lakehouse architectures for large-scale data management.
- Apply distributed storage concepts to design data organization schema management and partitioning strategies for big data.
- Analyze storage architectures to evaluate performance reliability scalability and cost-effectiveness for organizational needs.
Data pipelines
- Describe data pipeline architectures including ETL ELT streaming ingestion and data orchestration for processing workflows.
- Apply pipeline design patterns to implement data flow from source through processing to consumption using appropriate tools.
- Analyze pipeline architectures to identify bottlenecks reliability issues and optimization opportunities for data workflows.
3
Data Processing
2 topics
Batch processing
- Describe batch processing frameworks including MapReduce Spark and distributed SQL engines for large-scale data transformation.
- Apply batch processing concepts to understand job scheduling resource management and output handling for analytical workloads.
- Analyze batch processing performance to evaluate execution efficiency resource utilization and optimization opportunities.
Stream processing
- Describe stream processing frameworks including Kafka Streams Flink and Spark Streaming for real-time data analysis.
- Apply stream processing concepts to understand windowing event-time processing and exactly-once semantics for real-time analytics.
- Analyze streaming architecture suitability to evaluate latency requirements throughput needs and consistency guarantees.
4
NoSQL Databases
2 topics
NoSQL types
- Describe NoSQL database categories including document key-value column-family and graph databases with their data models and use cases.
- Apply NoSQL selection criteria to determine appropriate database type based on data model query patterns and consistency requirements.
- Analyze NoSQL database trade-offs including CAP theorem consistency models and performance characteristics for applications.
Database operations
- Describe NoSQL database operations including data modeling indexing replication and cluster management for distributed environments.
- Apply NoSQL operational concepts to understand scaling sharding replication topology and backup recovery procedures.
- Analyze NoSQL operational challenges to evaluate performance bottlenecks consistency issues and availability concerns.
5
Cloud Big Data
2 topics
Cloud platforms
- Describe cloud big data services including AWS EMR Azure HDInsight GCP Dataproc and managed analytics platforms.
- Apply cloud big data services to implement scalable analytics using serverless processing managed databases and auto-scaling.
- Analyze cloud big data architectures to evaluate cost optimization scalability and performance for organizational workloads.
Serverless analytics
- Describe serverless analytics including Athena BigQuery Synapse and their on-demand processing models for ad-hoc analysis.
- Apply serverless analytics to implement cost-effective query processing data exploration and reporting without infrastructure management.
- Analyze serverless analytics suitability to evaluate cost patterns performance limitations and use case appropriateness.
6
Data Analytics
2 topics
Analytics methods
- Describe analytics methods including descriptive diagnostic predictive and prescriptive analytics and their business applications.
- Apply analytics methods to implement reporting dashboards trend analysis and data-driven decision support for organizations.
- Analyze analytics outcomes to evaluate insight quality decision impact and continuous improvement for analytical processes.
Visualization
- Describe data visualization tools and techniques including dashboards charts graphs and interactive exploration for data communication.
- Apply visualization best practices to create effective data presentations that communicate insights to technical and business audiences.
- Analyze visualization effectiveness to evaluate clarity accuracy and stakeholder comprehension for communication improvement.
7
Big Data Security
2 topics
Access control
- Describe big data access control including RBAC ABAC row-level security and column-level encryption for data protection.
- Apply access control to implement fine-grained permissions data masking and audit logging in big data environments.
- Analyze access control effectiveness to evaluate permission granularity enforcement consistency and audit trail completeness.
Encryption and privacy
- Describe big data encryption including at-rest in-transit field-level and format-preserving encryption for data protection.
- Apply encryption and privacy controls including data masking tokenization and differential privacy for big data environments.
- Analyze data protection measures to evaluate encryption coverage privacy compliance and protection against insider threats.
8
Data Governance
2 topics
Governance frameworks
- Describe data governance frameworks including DAMA DMBOK data stewardship and quality management for organizational data.
- Apply data governance to implement metadata management data catalogs lineage tracking and quality monitoring processes.
- Analyze governance effectiveness to evaluate data quality compliance adherence and organizational data management maturity.
Compliance
- Describe data compliance requirements including GDPR CCPA HIPAA and sector-specific regulations for big data processing.
- Apply compliance requirements to implement consent management data retention and cross-border transfer controls for big data.
- Analyze compliance posture to identify gaps in data handling documentation and regulatory adherence for remediation.
9
ML Integration
2 topics
ML pipelines
- Describe machine learning integration with big data including feature stores model training platforms and prediction serving architectures.
- Apply ML integration patterns to understand feature engineering model training and scoring within big data processing pipelines.
- Analyze ML integration architectures to evaluate scalability latency and model management for production big data systems.
MLOps for big data
- Describe MLOps practices for big data including model versioning experiment tracking and automated retraining in distributed environments.
- Apply MLOps concepts to understand model deployment monitoring and lifecycle management within big data infrastructure.
- Analyze MLOps maturity to evaluate automation coverage monitoring effectiveness and pipeline reliability for improvements.
10
Consulting and Strategy
2 topics
Assessment methodology
- Describe big data readiness assessment including data maturity technology evaluation and organizational capability analysis.
- Apply assessment methodology to evaluate client big data capabilities identify gaps and develop improvement recommendations.
- Analyze assessment results to prioritize recommendations estimate investment requirements and develop implementation roadmaps.
Strategy development
- Describe big data strategy development including technology selection roadmap creation and organizational change management.
- Apply strategy development to create actionable big data adoption plans with phased implementation and success metrics.
- Analyze strategy effectiveness to evaluate adoption progress technology utilization and business value achievement.
Scope
Included Topics
- EC-Council BDC covering big data concepts architectures analytics platforms data governance and security for big data environments.
- Big data fundamentals including volume velocity variety veracity and value characteristics and technology ecosystems.
- Big data architectures including Hadoop ecosystem distributed computing and cloud-based big data platforms.
- Data analytics including batch processing stream processing machine learning integration and visualization techniques.
- Big data security including access control encryption data masking and privacy compliance for large-scale data systems.
Not Covered
- AI and machine learning covered by AIE/CAIPM.
- Offensive security testing covered by CEH.
- Enterprise security governance covered by CCISO.
- Network defense covered by CND.
Official Exam Page
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